add affine transforms and test (#2145)

Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>

src/lerobot/datasets/transforms.py

@@ -206,6 +206,11 @@ class ImageTransformsConfig:
                 type="SharpnessJitter",
                 kwargs={"sharpness": (0.5, 1.5)},
             ),
+            "affine": ImageTransformConfig(
+                weight=1.0,
+                type="RandomAffine",
+                kwargs={"degrees": (-5.0, 5.0), "translate": (0.05, 0.05)},
+            ),
         }
     )
 
@@ -217,6 +222,8 @@ def make_transform_from_config(cfg: ImageTransformConfig):
         return v2.ColorJitter(**cfg.kwargs)
     elif cfg.type == "SharpnessJitter":
         return SharpnessJitter(**cfg.kwargs)
+    elif cfg.type == "RandomAffine":
+        return v2.RandomAffine(**cfg.kwargs)
     else:
         raise ValueError(f"Transform '{cfg.type}' is not valid.")
 

tests/datasets/test_image_transforms.py

@@ -134,6 +134,25 @@ def test_get_image_transforms_sharpness(img_tensor_factory, min_max):
     torch.testing.assert_close(tf_actual(img_tensor), tf_expected(img_tensor))
 
 
+@pytest.mark.parametrize("degrees, translate", [((-5.0, 5.0), (0.05, 0.05)), ((10.0, 10.0), (0.1, 0.1))])
+def test_get_image_transforms_affine(img_tensor_factory, degrees, translate):
+    img_tensor = img_tensor_factory()
+    tf_cfg = ImageTransformsConfig(
+        enable=True,
+        tfs={
+            "affine": ImageTransformConfig(
+                type="RandomAffine", kwargs={"degrees": degrees, "translate": translate}
+            )
+        },
+    )
+    tf = ImageTransforms(tf_cfg)
+    output = tf(img_tensor)
+    # Verify output shape is preserved
+    assert output.shape == img_tensor.shape
+    # Verify transform is type RandomAffine
+    assert isinstance(tf.transforms["affine"], v2.RandomAffine)
+
+
 def test_get_image_transforms_max_num_transforms(img_tensor_factory):
     img_tensor = img_tensor_factory()
     tf_cfg = ImageTransformsConfig(
@@ -262,7 +281,37 @@ def test_backward_compatibility_default_config(img_tensor, default_transforms):
     # NOTE: PyTorch versions have different randomness, it might break this test.
     # See this PR: https://github.com/huggingface/lerobot/pull/1127.
 
-    cfg = ImageTransformsConfig(enable=True)
+    # Use config without affine to match original test artifacts
+    cfg = ImageTransformsConfig(
+        enable=True,
+        tfs={
+            "brightness": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"brightness": (0.8, 1.2)},
+            ),
+            "contrast": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"contrast": (0.8, 1.2)},
+            ),
+            "saturation": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"saturation": (0.5, 1.5)},
+            ),
+            "hue": ImageTransformConfig(
+                weight=1.0,
+                type="ColorJitter",
+                kwargs={"hue": (-0.05, 0.05)},
+            ),
+            "sharpness": ImageTransformConfig(
+                weight=1.0,
+                type="SharpnessJitter",
+                kwargs={"sharpness": (0.5, 1.5)},
+            ),
+        },
+    )
     default_tf = ImageTransforms(cfg)
 
     with seeded_context(1337):
@@ -368,7 +417,7 @@ def test_save_each_transform(img_tensor_factory, tmp_path):
     save_each_transform(tf_cfg, img_tensor, tmp_path, n_examples)
 
     # Check if the transformed images exist for each transform type
-    transforms = ["brightness", "contrast", "saturation", "hue", "sharpness"]
+    transforms = ["brightness", "contrast", "saturation", "hue", "sharpness", "affine"]
     for transform in transforms:
         transform_dir = tmp_path / transform
         assert transform_dir.exists(), f"{transform} directory was not created."